Dataset Information

Testing novel facial recognition technology to identify dogs during vaccination campaigns.

ABSTRACT: A lack of methods to identify individual animals can be a barrier to zoonoses control. We developed and field-tested facial recognition technology for a mobile phone application to identify dogs, which we used to assess vaccination coverage against rabies in rural Tanzania. Dogs were vaccinated, registered using the application, and microchipped. During subsequent household visits to validate vaccination, dogs were registered using the application and their vaccination status determined by operators using the application to classify dogs as vaccinated (matched) or unvaccinated (unmatched), with microchips validating classifications. From 534 classified dogs (251 vaccinated, 283 unvaccinated), the application specificity was 98.9% and sensitivity 76.2%, with positive and negative predictive values of 98.4% and 82.8% respectively. The facial recognition algorithm correctly matched 249 (99.2%) vaccinated and microchipped dogs (true positives) and failed to match two (0.8%) vaccinated dogs (false negatives). Operators correctly identified 186 (74.1%) vaccinated dogs (true positives), and 280 (98.9%) unvaccinated dogs (true negatives), but incorrectly classified 58 (23.1%) vaccinated dogs as unmatched (false negatives). Reduced application sensitivity resulted from poor quality photos and light-associated color distortion. With development and operator training, this technology has potential to be a useful tool to identify dogs and support research and intervention programs.

SUBMITTER: Czupryna AM

PROVIDER: S-EPMC10716125 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Testing novel facial recognition technology to identify dogs during vaccination campaigns.

Czupryna Anna Maria AM Estepho Mike M Lugelo Ahmed A Bigambo Machunde M Sambo Maganga M Changalucha Joel J Lushasi Kennedy Selestin KS Rooyakkers Philip P Hampson Katie K Lankester Felix F

Scientific reports 20231212 1

A lack of methods to identify individual animals can be a barrier to zoonoses control. We developed and field-tested facial recognition technology for a mobile phone application to identify dogs, which we used to assess vaccination coverage against rabies in rural Tanzania. Dogs were vaccinated, registered using the application, and microchipped. During subsequent household visits to validate vaccination, dogs were registered using the application and their vaccination status determined by opera ...[more]

PMID: 38086911

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Project description:Background There is limited literature on security and access for social care settings despite policy highlighting importance, and no published research exploring facial recognition lock technology (FRLT) for potential improvements. This study explored FRLT device implementation, use, barriers and benefits. Methods One residential care home with 43 older adults and 68 staff members (Site A), and one supported living facility caring for six individuals with mental health issues with 18 staff members (Site B) were provided with FRLT for six months. Nine pre-implementation staff interviews explored existing access and security perceptions. Ten post-implementation staff interviews and one staff focus group were conducted; all were analysed using content analysis to understand, alongside process mapping, the use and impact of the FRLT. Interview participants included site care staff and other visiting healthcare professionals. We additionally report feedback from the technology developers to demonstrate impact of industry-academia collaboration. Results Pre-implementation interviews highlighted issues with current pin-pad or lock-box systems, including; code sharing; code visibility, ineffective code changes, security issues following high staff turnover, lack of efficiency for visitors including NHS staff and lack of infection control suggesting requirement for innovation and improvement. Pre-implementation interviews showed openness and interest in FRLT, although initial queries were raised around cost effectiveness and staff skills. Following implementation, good levels of adoption were achieved with 72% and 100% (49/68 and 18/18) of staff members uploading their face at the two sites, and 100% of residents at Site B using the system (6/6). Additionally, Site B made a positive procurement decision and continues to discuss wider rollout. Post implementation interviews suggested FRLT was useful and acceptable for improving security and access. Benefits identified included staff/visitor time saving, enhanced security, team ease of access, resident autonomy and fewer shared touch points. Integration was suggested including with fire alarm systems, staff clocking in/out, and Covid monitoring to improve usefulness. The developers have since responded to feedback with design iterations. Conclusion We identified concerns on security and access in social care settings, which warrant further exploration and research. FRLT could increase resident autonomy and reduce staff burden, particularly considering frequent multi-agency health and care visits.

Project description:Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.

Project description:BackgroundThe COVID-19 pandemic has disrupted the delivery of immunisation services globally. Many countries have postponed vaccination campaigns out of concern about infection risks to the staff delivering vaccination, the children being vaccinated, and their families. The World Health Organization recommends considering both the benefit of preventive campaigns and the risk of SARS-CoV-2 transmission when making decisions about campaigns during COVID-19 outbreaks, but there has been little quantification of the risks.MethodsWe modelled excess SARS-CoV-2 infection risk to vaccinators, vaccinees, and their caregivers resulting from vaccination campaigns delivered during a COVID-19 epidemic. Our model used population age structure and contact patterns from three exemplar countries (Burkina Faso, Ethiopia, and Brazil). It combined an existing compartmental transmission model of an underlying COVID-19 epidemic with a Reed-Frost model of SARS-CoV-2 infection risk to vaccinators and vaccinees. We explored how excess risk depends on key parameters governing SARS-CoV-2 transmissibility, and aspects of campaign delivery such as campaign duration, number of vaccinations, and effectiveness of personal protective equipment (PPE) and symptomatic screening.ResultsInfection risks differ considerably depending on the circumstances in which vaccination campaigns are conducted. A campaign conducted at the peak of a SARS-CoV-2 epidemic with high prevalence and without special infection mitigation measures could increase absolute infection risk by 32 to 45% for vaccinators and 0.3 to 0.5% for vaccinees and caregivers. However, these risks could be reduced to 3.6 to 5.3% and 0.1 to 0.2% respectively by use of PPE that reduces transmission by 90% (as might be achieved with N95 respirators or high-quality surgical masks) and symptomatic screening.ConclusionsSARS-CoV-2 infection risks to vaccinators, vaccinees, and caregivers during vaccination campaigns can be greatly reduced by adequate PPE, symptomatic screening, and appropriate campaign timing. Our results support the use of adequate risk mitigation measures for vaccination campaigns held during SARS-CoV-2 epidemics, rather than cancelling them entirely.

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Testing novel facial recognition technology to identify dogs during vaccination campaigns.

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Testing novel facial recognition technology to identify dogs during vaccination campaigns.

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